Overview

Brought to you by YData

Dataset statistics

Number of variables33
Number of observations115609
Missing cells70316
Missing cells (%)1.8%
Total size in memory29.1 MiB
Average record size in memory264.0 B

Variable types

Text18
Numeric15

Alerts

order_delivered_carrier_date has 1195 (1.0%) missing values Missing
order_delivered_customer_date has 2400 (2.1%) missing values Missing
review_comment_message has 66703 (57.7%) missing values Missing

Reproduction

Analysis started2025-08-06 08:23:12.620843
Analysis finished2025-08-06 08:23:21.865569
Duration9.24 seconds
Software versionydata-profiling vv4.16.1
Download configurationconfig.json

Variables

Distinct96516
Distinct (%)83.5%
Missing0
Missing (%)0.0%
Memory size903.3 KiB
2025-08-06T13:53:22.042994image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length32
Median length32
Mean length32
Min length32

Characters and Unicode

Total characters3699488
Distinct characters16
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique83927 ?
Unique (%)72.6%

Sample

1st rowe481f51cbdc54678b7cc49136f2d6af7
2nd rowe481f51cbdc54678b7cc49136f2d6af7
3rd rowe481f51cbdc54678b7cc49136f2d6af7
4th row128e10d95713541c87cd1a2e48201934
5th row0e7e841ddf8f8f2de2bad69267ecfbcf
ValueCountFrequency (%)
895ab968e7bb0d5659d16cd74cd1650c 63
 
0.1%
fedcd9f7ccdc8cba3a18defedd1a5547 38
 
< 0.1%
fa65dad1b0e818e3ccc5cb0e39231352 29
 
< 0.1%
ccf804e764ed5650cd8759557269dc13 26
 
< 0.1%
a3725dfe487d359b5be08cac48b64ec5 24
 
< 0.1%
6d58638e32674bebee793a47ac4cbadc 24
 
< 0.1%
68986e4324f6a21481df4e6e89abcf01 24
 
< 0.1%
c6492b842ac190db807c15aff21a7dd6 24
 
< 0.1%
465c2e1bee4561cb39e0db8c5993aafc 24
 
< 0.1%
5a3b1c29a49756e75f1ef513383c0c12 22
 
< 0.1%
Other values (96506) 115311
99.7%
2025-08-06T13:53:22.301545image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
4 232347
 
6.3%
b 232260
 
6.3%
6 232183
 
6.3%
e 231974
 
6.3%
c 231513
 
6.3%
3 231489
 
6.3%
1 231448
 
6.3%
7 231435
 
6.3%
8 231366
 
6.3%
a 231122
 
6.2%
Other values (6) 1382351
37.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3699488
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
4 232347
 
6.3%
b 232260
 
6.3%
6 232183
 
6.3%
e 231974
 
6.3%
c 231513
 
6.3%
3 231489
 
6.3%
1 231448
 
6.3%
7 231435
 
6.3%
8 231366
 
6.3%
a 231122
 
6.2%
Other values (6) 1382351
37.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3699488
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
4 232347
 
6.3%
b 232260
 
6.3%
6 232183
 
6.3%
e 231974
 
6.3%
c 231513
 
6.3%
3 231489
 
6.3%
1 231448
 
6.3%
7 231435
 
6.3%
8 231366
 
6.3%
a 231122
 
6.2%
Other values (6) 1382351
37.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3699488
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
4 232347
 
6.3%
b 232260
 
6.3%
6 232183
 
6.3%
e 231974
 
6.3%
c 231513
 
6.3%
3 231489
 
6.3%
1 231448
 
6.3%
7 231435
 
6.3%
8 231366
 
6.3%
a 231122
 
6.2%
Other values (6) 1382351
37.4%
Distinct96516
Distinct (%)83.5%
Missing0
Missing (%)0.0%
Memory size903.3 KiB
2025-08-06T13:53:22.481593image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length32
Median length32
Mean length32
Min length32

Characters and Unicode

Total characters3699488
Distinct characters16
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique83927 ?
Unique (%)72.6%

Sample

1st row9ef432eb6251297304e76186b10a928d
2nd row9ef432eb6251297304e76186b10a928d
3rd row9ef432eb6251297304e76186b10a928d
4th rowa20e8105f23924cd00833fd87daa0831
5th row26c7ac168e1433912a51b924fbd34d34
ValueCountFrequency (%)
270c23a11d024a44c896d1894b261a83 63
 
0.1%
13aa59158da63ba0e93ec6ac2c07aacb 38
 
< 0.1%
9af2372a1e49340278e7c1ef8d749f34 29
 
< 0.1%
92cd3ec6e2d643d4ebd0e3d6238f69e2 26
 
< 0.1%
d22f25a9fadfb1abbc2e29395b1239f4 24
 
< 0.1%
2ba91e12e5e4c9f56b82b86d9031d329 24
 
< 0.1%
86cc80fef09f7f39df4b0dbce48e81cb 24
 
< 0.1%
6ee2f17e3b6c33d6a9557f280edd2925 24
 
< 0.1%
63b964e79dee32a3587651701a2b8dbf 24
 
< 0.1%
be1c4e52bb71e0c54b11a26b8e8d59f2 22
 
< 0.1%
Other values (96506) 115311
99.7%
2025-08-06T13:53:22.733626image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2 231848
 
6.3%
f 231815
 
6.3%
5 231684
 
6.3%
c 231607
 
6.3%
6 231574
 
6.3%
1 231546
 
6.3%
e 231206
 
6.2%
8 231191
 
6.2%
a 231174
 
6.2%
d 231155
 
6.2%
Other values (6) 1384688
37.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3699488
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 231848
 
6.3%
f 231815
 
6.3%
5 231684
 
6.3%
c 231607
 
6.3%
6 231574
 
6.3%
1 231546
 
6.3%
e 231206
 
6.2%
8 231191
 
6.2%
a 231174
 
6.2%
d 231155
 
6.2%
Other values (6) 1384688
37.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3699488
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 231848
 
6.3%
f 231815
 
6.3%
5 231684
 
6.3%
c 231607
 
6.3%
6 231574
 
6.3%
1 231546
 
6.3%
e 231206
 
6.2%
8 231191
 
6.2%
a 231174
 
6.2%
d 231155
 
6.2%
Other values (6) 1384688
37.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3699488
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 231848
 
6.3%
f 231815
 
6.3%
5 231684
 
6.3%
c 231607
 
6.3%
6 231574
 
6.3%
1 231546
 
6.3%
e 231206
 
6.2%
8 231191
 
6.2%
a 231174
 
6.2%
d 231155
 
6.2%
Other values (6) 1384688
37.4%
Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size903.3 KiB
2025-08-06T13:53:22.806549image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length11
Median length9
Mean length8.975763133
Min length7

Characters and Unicode

Total characters1037679
Distinct characters16
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowdelivered
2nd rowdelivered
3rd rowdelivered
4th rowdelivered
5th rowdelivered
ValueCountFrequency (%)
delivered 113210
97.9%
shipped 1138
 
1.0%
canceled 536
 
0.5%
invoiced 358
 
0.3%
processing 357
 
0.3%
unavailable 7
 
< 0.1%
approved 3
 
< 0.1%
2025-08-06T13:53:23.094134image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 342565
33.0%
d 228455
22.0%
i 115428
 
11.1%
l 113760
 
11.0%
v 113578
 
10.9%
r 113570
 
10.9%
p 2639
 
0.3%
s 1852
 
0.2%
c 1787
 
0.2%
n 1258
 
0.1%
Other values (6) 2787
 
0.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1037679
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 342565
33.0%
d 228455
22.0%
i 115428
 
11.1%
l 113760
 
11.0%
v 113578
 
10.9%
r 113570
 
10.9%
p 2639
 
0.3%
s 1852
 
0.2%
c 1787
 
0.2%
n 1258
 
0.1%
Other values (6) 2787
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1037679
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 342565
33.0%
d 228455
22.0%
i 115428
 
11.1%
l 113760
 
11.0%
v 113578
 
10.9%
r 113570
 
10.9%
p 2639
 
0.3%
s 1852
 
0.2%
c 1787
 
0.2%
n 1258
 
0.1%
Other values (6) 2787
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1037679
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 342565
33.0%
d 228455
22.0%
i 115428
 
11.1%
l 113760
 
11.0%
v 113578
 
10.9%
r 113570
 
10.9%
p 2639
 
0.3%
s 1852
 
0.2%
c 1787
 
0.2%
n 1258
 
0.1%
Other values (6) 2787
 
0.3%
Distinct95989
Distinct (%)83.0%
Missing0
Missing (%)0.0%
Memory size903.3 KiB
2025-08-06T13:53:23.251198image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length19
Median length19
Mean length19
Min length19

Characters and Unicode

Total characters2196571
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique83144 ?
Unique (%)71.9%

Sample

1st row2017-10-02 10:56:33
2nd row2017-10-02 10:56:33
3rd row2017-10-02 10:56:33
4th row2017-08-15 18:29:31
5th row2017-08-02 18:24:47
ValueCountFrequency (%)
2017-11-24 1398
 
0.6%
2017-11-25 624
 
0.3%
2017-11-27 495
 
0.2%
2017-11-26 468
 
0.2%
2018-08-06 443
 
0.2%
2017-11-28 441
 
0.2%
2018-05-15 436
 
0.2%
2018-08-07 434
 
0.2%
2018-05-07 424
 
0.2%
2018-05-14 421
 
0.2%
Other values (50780) 225634
97.6%
2025-08-06T13:53:23.483134image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 357455
16.3%
0 355834
16.2%
2 281971
12.8%
- 231218
10.5%
: 231218
10.5%
8 120237
 
5.5%
115609
 
5.3%
7 107338
 
4.9%
3 102100
 
4.6%
5 93497
 
4.3%
Other values (3) 200094
9.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2196571
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 357455
16.3%
0 355834
16.2%
2 281971
12.8%
- 231218
10.5%
: 231218
10.5%
8 120237
 
5.5%
115609
 
5.3%
7 107338
 
4.9%
3 102100
 
4.6%
5 93497
 
4.3%
Other values (3) 200094
9.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2196571
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 357455
16.3%
0 355834
16.2%
2 281971
12.8%
- 231218
10.5%
: 231218
10.5%
8 120237
 
5.5%
115609
 
5.3%
7 107338
 
4.9%
3 102100
 
4.6%
5 93497
 
4.3%
Other values (3) 200094
9.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2196571
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 357455
16.3%
0 355834
16.2%
2 281971
12.8%
- 231218
10.5%
: 231218
10.5%
8 120237
 
5.5%
115609
 
5.3%
7 107338
 
4.9%
3 102100
 
4.6%
5 93497
 
4.3%
Other values (3) 200094
9.1%
Distinct88332
Distinct (%)76.4%
Missing14
Missing (%)< 0.1%
Memory size903.3 KiB
2025-08-06T13:53:23.650097image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length19
Median length19
Mean length19
Min length19

Characters and Unicode

Total characters2196305
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique70812 ?
Unique (%)61.3%

Sample

1st row2017-10-02 11:07:15
2nd row2017-10-02 11:07:15
3rd row2017-10-02 11:07:15
4th row2017-08-15 20:05:16
5th row2017-08-02 18:43:15
ValueCountFrequency (%)
2018-04-24 1149
 
0.5%
2017-11-24 977
 
0.4%
2017-11-25 913
 
0.4%
2018-07-05 816
 
0.4%
2017-11-28 591
 
0.3%
2018-08-07 500
 
0.2%
2018-05-08 489
 
0.2%
2018-08-20 485
 
0.2%
2018-05-01 478
 
0.2%
2017-12-05 468
 
0.2%
Other values (41742) 224324
97.0%
2025-08-06T13:53:23.883331image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 370476
16.9%
1 355626
16.2%
2 280851
12.8%
- 231190
10.5%
: 231190
10.5%
115595
 
5.3%
8 114212
 
5.2%
5 111490
 
5.1%
3 108369
 
4.9%
7 102415
 
4.7%
Other values (3) 174891
8.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2196305
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 370476
16.9%
1 355626
16.2%
2 280851
12.8%
- 231190
10.5%
: 231190
10.5%
115595
 
5.3%
8 114212
 
5.2%
5 111490
 
5.1%
3 108369
 
4.9%
7 102415
 
4.7%
Other values (3) 174891
8.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2196305
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 370476
16.9%
1 355626
16.2%
2 280851
12.8%
- 231190
10.5%
: 231190
10.5%
115595
 
5.3%
8 114212
 
5.2%
5 111490
 
5.1%
3 108369
 
4.9%
7 102415
 
4.7%
Other values (3) 174891
8.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2196305
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 370476
16.9%
1 355626
16.2%
2 280851
12.8%
- 231190
10.5%
: 231190
10.5%
115595
 
5.3%
8 114212
 
5.2%
5 111490
 
5.1%
3 108369
 
4.9%
7 102415
 
4.7%
Other values (3) 174891
8.0%
Distinct79241
Distinct (%)69.3%
Missing1195
Missing (%)1.0%
Memory size903.3 KiB
2025-08-06T13:53:24.070473image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length19
Median length19
Mean length19
Min length19

Characters and Unicode

Total characters2173866
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique60052 ?
Unique (%)52.5%

Sample

1st row2017-10-04 19:55:00
2nd row2017-10-04 19:55:00
3rd row2017-10-04 19:55:00
4th row2017-08-17 15:28:33
5th row2017-08-04 17:35:43
ValueCountFrequency (%)
2017-11-28 911
 
0.4%
2017-11-27 791
 
0.3%
2017-11-29 693
 
0.3%
2018-02-27 614
 
0.3%
2018-03-27 613
 
0.3%
2018-08-06 590
 
0.3%
2017-11-30 568
 
0.2%
2018-05-14 562
 
0.2%
2018-08-13 545
 
0.2%
2018-05-03 533
 
0.2%
Other values (37035) 222408
97.2%
2025-08-06T13:53:24.323672image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 396794
18.3%
1 338882
15.6%
2 269331
12.4%
- 228828
10.5%
: 228828
10.5%
8 120918
 
5.6%
114414
 
5.3%
7 104065
 
4.8%
3 96216
 
4.4%
4 89865
 
4.1%
Other values (3) 185725
8.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2173866
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 396794
18.3%
1 338882
15.6%
2 269331
12.4%
- 228828
10.5%
: 228828
10.5%
8 120918
 
5.6%
114414
 
5.3%
7 104065
 
4.8%
3 96216
 
4.4%
4 89865
 
4.1%
Other values (3) 185725
8.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2173866
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 396794
18.3%
1 338882
15.6%
2 269331
12.4%
- 228828
10.5%
: 228828
10.5%
8 120918
 
5.6%
114414
 
5.3%
7 104065
 
4.8%
3 96216
 
4.4%
4 89865
 
4.1%
Other values (3) 185725
8.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2173866
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 396794
18.3%
1 338882
15.6%
2 269331
12.4%
- 228828
10.5%
: 228828
10.5%
8 120918
 
5.6%
114414
 
5.3%
7 104065
 
4.8%
3 96216
 
4.4%
4 89865
 
4.1%
Other values (3) 185725
8.5%
Distinct93702
Distinct (%)82.8%
Missing2400
Missing (%)2.1%
Memory size903.3 KiB
2025-08-06T13:53:24.526749image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length19
Median length19
Mean length19
Min length19

Characters and Unicode

Total characters2150971
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique80761 ?
Unique (%)71.3%

Sample

1st row2017-10-10 21:25:13
2nd row2017-10-10 21:25:13
3rd row2017-10-10 21:25:13
4th row2017-08-18 14:44:43
5th row2017-08-07 18:30:01
ValueCountFrequency (%)
2018-05-21 523
 
0.2%
2018-05-14 521
 
0.2%
2018-05-18 502
 
0.2%
2018-08-13 502
 
0.2%
2018-08-27 500
 
0.2%
2018-05-03 493
 
0.2%
2017-12-11 481
 
0.2%
2018-04-11 481
 
0.2%
2018-08-23 478
 
0.2%
2018-04-30 478
 
0.2%
Other values (41303) 221459
97.8%
2025-08-06T13:53:24.777039image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 332609
15.5%
0 329367
15.3%
2 284779
13.2%
- 226418
10.5%
: 226418
10.5%
8 132920
 
6.2%
113209
 
5.3%
7 104753
 
4.9%
3 104638
 
4.9%
4 98043
 
4.6%
Other values (3) 197817
9.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2150971
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 332609
15.5%
0 329367
15.3%
2 284779
13.2%
- 226418
10.5%
: 226418
10.5%
8 132920
 
6.2%
113209
 
5.3%
7 104753
 
4.9%
3 104638
 
4.9%
4 98043
 
4.6%
Other values (3) 197817
9.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2150971
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 332609
15.5%
0 329367
15.3%
2 284779
13.2%
- 226418
10.5%
: 226418
10.5%
8 132920
 
6.2%
113209
 
5.3%
7 104753
 
4.9%
3 104638
 
4.9%
4 98043
 
4.6%
Other values (3) 197817
9.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2150971
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 332609
15.5%
0 329367
15.3%
2 284779
13.2%
- 226418
10.5%
: 226418
10.5%
8 132920
 
6.2%
113209
 
5.3%
7 104753
 
4.9%
3 104638
 
4.9%
4 98043
 
4.6%
Other values (3) 197817
9.2%
Distinct449
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size903.3 KiB
2025-08-06T13:53:24.911188image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length19
Median length19
Mean length19
Min length19

Characters and Unicode

Total characters2196571
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique19 ?
Unique (%)< 0.1%

Sample

1st row2017-10-18 00:00:00
2nd row2017-10-18 00:00:00
3rd row2017-10-18 00:00:00
4th row2017-08-28 00:00:00
5th row2017-08-15 00:00:00
ValueCountFrequency (%)
00:00:00 115609
50.0%
2017-12-20 649
 
0.3%
2018-03-12 605
 
0.3%
2018-05-29 599
 
0.3%
2018-03-13 596
 
0.3%
2018-07-16 586
 
0.3%
2018-07-05 583
 
0.3%
2018-05-30 580
 
0.3%
2018-05-28 578
 
0.2%
2017-12-19 570
 
0.2%
Other values (440) 110263
47.7%
2025-08-06T13:53:25.124640image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 956195
43.5%
- 231218
 
10.5%
: 231218
 
10.5%
1 197856
 
9.0%
2 180213
 
8.2%
115609
 
5.3%
8 95835
 
4.4%
7 70487
 
3.2%
3 30921
 
1.4%
5 23800
 
1.1%
Other values (3) 63219
 
2.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2196571
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 956195
43.5%
- 231218
 
10.5%
: 231218
 
10.5%
1 197856
 
9.0%
2 180213
 
8.2%
115609
 
5.3%
8 95835
 
4.4%
7 70487
 
3.2%
3 30921
 
1.4%
5 23800
 
1.1%
Other values (3) 63219
 
2.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2196571
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 956195
43.5%
- 231218
 
10.5%
: 231218
 
10.5%
1 197856
 
9.0%
2 180213
 
8.2%
115609
 
5.3%
8 95835
 
4.4%
7 70487
 
3.2%
3 30921
 
1.4%
5 23800
 
1.1%
Other values (3) 63219
 
2.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2196571
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 956195
43.5%
- 231218
 
10.5%
: 231218
 
10.5%
1 197856
 
9.0%
2 180213
 
8.2%
115609
 
5.3%
8 95835
 
4.4%
7 70487
 
3.2%
3 30921
 
1.4%
5 23800
 
1.1%
Other values (3) 63219
 
2.9%

payment_sequential
Real number (ℝ)

Distinct29
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.093747027
Minimum1
Maximum29
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size903.3 KiB
2025-08-06T13:53:25.191681image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q31
95-th percentile1
Maximum29
Range28
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.7298487086
Coefficient of variation (CV)0.6672920619
Kurtosis350.4402059
Mean1.093747027
Median Absolute Deviation (MAD)0
Skewness16.00176826
Sum126447
Variance0.5326791375
MonotonicityNot monotonic
2025-08-06T13:53:25.270597image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
1 110662
95.7%
2 3298
 
2.9%
3 636
 
0.6%
4 307
 
0.3%
5 183
 
0.2%
6 127
 
0.1%
7 88
 
0.1%
8 58
 
0.1%
9 47
 
< 0.1%
10 40
 
< 0.1%
Other values (19) 163
 
0.1%
ValueCountFrequency (%)
1 110662
95.7%
2 3298
 
2.9%
3 636
 
0.6%
4 307
 
0.3%
5 183
 
0.2%
ValueCountFrequency (%)
29 1
< 0.1%
28 1
< 0.1%
27 1
< 0.1%
26 2
< 0.1%
25 2
< 0.1%
Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size903.3 KiB
2025-08-06T13:53:25.346457image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length11
Median length11
Mean length9.798908389
Min length6

Characters and Unicode

Total characters1132842
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowcredit_card
2nd rowvoucher
3rd rowvoucher
4th rowcredit_card
5th rowcredit_card
ValueCountFrequency (%)
credit_card 85278
73.8%
boleto 22510
 
19.5%
voucher 6162
 
5.3%
debit_card 1659
 
1.4%
2025-08-06T13:53:25.479157image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
c 178377
15.7%
r 178377
15.7%
d 173874
15.3%
e 115609
10.2%
t 109447
9.7%
i 86937
7.7%
_ 86937
7.7%
a 86937
7.7%
o 51182
 
4.5%
b 24169
 
2.1%
Other values (4) 40996
 
3.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1132842
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
c 178377
15.7%
r 178377
15.7%
d 173874
15.3%
e 115609
10.2%
t 109447
9.7%
i 86937
7.7%
_ 86937
7.7%
a 86937
7.7%
o 51182
 
4.5%
b 24169
 
2.1%
Other values (4) 40996
 
3.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1132842
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
c 178377
15.7%
r 178377
15.7%
d 173874
15.3%
e 115609
10.2%
t 109447
9.7%
i 86937
7.7%
_ 86937
7.7%
a 86937
7.7%
o 51182
 
4.5%
b 24169
 
2.1%
Other values (4) 40996
 
3.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1132842
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
c 178377
15.7%
r 178377
15.7%
d 173874
15.3%
e 115609
10.2%
t 109447
9.7%
i 86937
7.7%
_ 86937
7.7%
a 86937
7.7%
o 51182
 
4.5%
b 24169
 
2.1%
Other values (4) 40996
 
3.6%

payment_installments
Real number (ℝ)

Distinct24
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.94623256
Minimum0
Maximum24
Zeros3
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size903.3 KiB
2025-08-06T13:53:25.544778image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11
median2
Q34
95-th percentile10
Maximum24
Range24
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.781087117
Coefficient of variation (CV)0.9439469088
Kurtosis2.513768094
Mean2.94623256
Median Absolute Deviation (MAD)1
Skewness1.618171721
Sum340611
Variance7.734445554
MonotonicityNot monotonic
2025-08-06T13:53:25.616779image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
1 57599
49.8%
2 13404
 
11.6%
3 11551
 
10.0%
4 7855
 
6.8%
10 6785
 
5.9%
5 5928
 
5.1%
8 5013
 
4.3%
6 4546
 
3.9%
7 1789
 
1.5%
9 710
 
0.6%
Other values (14) 429
 
0.4%
ValueCountFrequency (%)
0 3
 
< 0.1%
1 57599
49.8%
2 13404
 
11.6%
3 11551
 
10.0%
4 7855
 
6.8%
ValueCountFrequency (%)
24 34
< 0.1%
23 1
 
< 0.1%
22 1
 
< 0.1%
21 6
 
< 0.1%
20 20
< 0.1%

payment_value
Real number (ℝ)

Distinct28657
Distinct (%)24.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean172.387379
Minimum0
Maximum13664.08
Zeros6
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size903.3 KiB
2025-08-06T13:53:25.700350image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile27.23
Q160.87
median108.05
Q3189.48
95-th percentile514.98
Maximum13664.08
Range13664.08
Interquartile range (IQR)128.61

Descriptive statistics

Standard deviation265.8739691
Coefficient of variation (CV)1.542305304
Kurtosis524.9452337
Mean172.387379
Median Absolute Deviation (MAD)56.67
Skewness14.30654423
Sum19929532.5
Variance70688.96742
MonotonicityNot monotonic
2025-08-06T13:53:25.793507image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
50 338
 
0.3%
100 285
 
0.2%
20 283
 
0.2%
77.57 249
 
0.2%
35 163
 
0.1%
73.34 157
 
0.1%
30 134
 
0.1%
116.94 130
 
0.1%
56.78 120
 
0.1%
155.14 119
 
0.1%
Other values (28647) 113631
98.3%
ValueCountFrequency (%)
0 6
< 0.1%
0.01 6
< 0.1%
0.03 2
 
< 0.1%
0.05 2
 
< 0.1%
0.08 2
 
< 0.1%
ValueCountFrequency (%)
13664.08 8
< 0.1%
7274.88 4
< 0.1%
6929.31 1
 
< 0.1%
6726.66 1
 
< 0.1%
6081.54 6
< 0.1%
Distinct93396
Distinct (%)80.8%
Missing0
Missing (%)0.0%
Memory size903.3 KiB
2025-08-06T13:53:25.985569image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length32
Median length32
Mean length32
Min length32

Characters and Unicode

Total characters3699488
Distinct characters16
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique79377 ?
Unique (%)68.7%

Sample

1st row7c396fd4830fd04220f754e42b4e5bff
2nd row7c396fd4830fd04220f754e42b4e5bff
3rd row7c396fd4830fd04220f754e42b4e5bff
4th row3a51803cc0d012c3b5dc8b7528cb05f7
5th rowef0996a1a279c26e7ecbd737be23d235
ValueCountFrequency (%)
9a736b248f67d166d2fbb006bcb877c3 75
 
0.1%
6fbc7cdadbb522125f4b27ae9dee4060 38
 
< 0.1%
f9ae226291893fda10af7965268fb7f6 35
 
< 0.1%
8af7ac63b2efbcbd88e5b11505e8098a 29
 
< 0.1%
569aa12b73b5f7edeaa6f2a01603e381 26
 
< 0.1%
c8460e4251689ba205045f3ea17884a1 24
 
< 0.1%
85963fd37bfd387aa6d915d8a1065486 24
 
< 0.1%
d97b3cfb22b0d6b25ac9ed4e9c2d481b 24
 
< 0.1%
90807fdb59eec2152bc977feeb6e47e7 24
 
< 0.1%
5419a7c9b86a43d8140e2939cd2c2f7e 24
 
< 0.1%
Other values (93386) 115286
99.7%
2025-08-06T13:53:26.230880image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
6 232253
 
6.3%
b 231885
 
6.3%
1 231813
 
6.3%
a 231488
 
6.3%
d 231353
 
6.3%
3 231341
 
6.3%
e 231271
 
6.3%
8 231179
 
6.2%
2 231112
 
6.2%
5 231087
 
6.2%
Other values (6) 1384706
37.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3699488
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
6 232253
 
6.3%
b 231885
 
6.3%
1 231813
 
6.3%
a 231488
 
6.3%
d 231353
 
6.3%
3 231341
 
6.3%
e 231271
 
6.3%
8 231179
 
6.2%
2 231112
 
6.2%
5 231087
 
6.2%
Other values (6) 1384706
37.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3699488
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
6 232253
 
6.3%
b 231885
 
6.3%
1 231813
 
6.3%
a 231488
 
6.3%
d 231353
 
6.3%
3 231341
 
6.3%
e 231271
 
6.3%
8 231179
 
6.2%
2 231112
 
6.2%
5 231087
 
6.2%
Other values (6) 1384706
37.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3699488
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
6 232253
 
6.3%
b 231885
 
6.3%
1 231813
 
6.3%
a 231488
 
6.3%
d 231353
 
6.3%
3 231341
 
6.3%
e 231271
 
6.3%
8 231179
 
6.2%
2 231112
 
6.2%
5 231087
 
6.2%
Other values (6) 1384706
37.4%

customer_zip_code_prefix
Real number (ℝ)

Distinct14907
Distinct (%)12.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean35061.5376
Minimum1003
Maximum99980
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size903.3 KiB
2025-08-06T13:53:26.313679image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1003
5-th percentile3286
Q111310
median24241
Q358745
95-th percentile90620
Maximum99980
Range98977
Interquartile range (IQR)47435

Descriptive statistics

Standard deviation29841.67173
Coefficient of variation (CV)0.8511227338
Kurtosis-0.7841017733
Mean35061.5376
Median Absolute Deviation (MAD)16231
Skewness0.7843642184
Sum4053429300
Variance890525371.8
MonotonicityNot monotonic
2025-08-06T13:53:26.411977image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
24220 154
 
0.1%
22793 151
 
0.1%
22790 150
 
0.1%
24230 138
 
0.1%
22775 126
 
0.1%
35162 124
 
0.1%
29101 112
 
0.1%
11740 110
 
0.1%
13087 107
 
0.1%
36570 104
 
0.1%
Other values (14897) 114333
98.9%
ValueCountFrequency (%)
1003 1
 
< 0.1%
1004 2
 
< 0.1%
1005 6
< 0.1%
1006 2
 
< 0.1%
1007 4
< 0.1%
ValueCountFrequency (%)
99980 3
< 0.1%
99970 1
 
< 0.1%
99965 2
< 0.1%
99960 1
 
< 0.1%
99955 3
< 0.1%
Distinct4093
Distinct (%)3.5%
Missing0
Missing (%)0.0%
Memory size903.3 KiB
2025-08-06T13:53:26.600710image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length32
Median length27
Mean length10.33285471
Min length3

Characters and Unicode

Total characters1194571
Distinct characters31
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1042 ?
Unique (%)0.9%

Sample

1st rowsao paulo
2nd rowsao paulo
3rd rowsao paulo
4th rowsao paulo
5th rowsao paulo
ValueCountFrequency (%)
sao 24620
 
12.1%
paulo 18348
 
9.1%
de 11267
 
5.6%
rio 9627
 
4.8%
janeiro 8022
 
4.0%
do 4964
 
2.4%
belo 3269
 
1.6%
horizonte 3224
 
1.6%
brasilia 2444
 
1.2%
porto 1942
 
1.0%
Other values (3268) 114916
56.7%
2025-08-06T13:53:26.867635image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 197038
16.5%
o 147371
12.3%
i 91350
 
7.6%
r 88530
 
7.4%
87034
 
7.3%
e 77539
 
6.5%
s 73205
 
6.1%
n 52923
 
4.4%
u 52440
 
4.4%
l 52072
 
4.4%
Other values (21) 275069
23.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1194571
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 197038
16.5%
o 147371
12.3%
i 91350
 
7.6%
r 88530
 
7.4%
87034
 
7.3%
e 77539
 
6.5%
s 73205
 
6.1%
n 52923
 
4.4%
u 52440
 
4.4%
l 52072
 
4.4%
Other values (21) 275069
23.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1194571
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 197038
16.5%
o 147371
12.3%
i 91350
 
7.6%
r 88530
 
7.4%
87034
 
7.3%
e 77539
 
6.5%
s 73205
 
6.1%
n 52923
 
4.4%
u 52440
 
4.4%
l 52072
 
4.4%
Other values (21) 275069
23.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1194571
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 197038
16.5%
o 147371
12.3%
i 91350
 
7.6%
r 88530
 
7.4%
87034
 
7.3%
e 77539
 
6.5%
s 73205
 
6.1%
n 52923
 
4.4%
u 52440
 
4.4%
l 52072
 
4.4%
Other values (21) 275069
23.0%
Distinct27
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size903.3 KiB
2025-08-06T13:53:26.935682image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters231218
Distinct characters17
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSP
2nd rowSP
3rd rowSP
4th rowSP
5th rowSP
ValueCountFrequency (%)
sp 48797
42.2%
rj 14987
 
13.0%
mg 13429
 
11.6%
rs 6413
 
5.5%
pr 5879
 
5.1%
sc 4218
 
3.6%
ba 3942
 
3.4%
df 2449
 
2.1%
go 2359
 
2.0%
es 2300
 
2.0%
Other values (17) 10836
 
9.4%
2025-08-06T13:53:27.083496image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
S 62966
27.2%
P 58871
25.5%
R 28218
12.2%
M 16380
 
7.1%
G 15788
 
6.8%
J 14987
 
6.5%
A 6654
 
2.9%
E 6071
 
2.6%
C 5838
 
2.5%
B 4561
 
2.0%
Other values (7) 10884
 
4.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 231218
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
S 62966
27.2%
P 58871
25.5%
R 28218
12.2%
M 16380
 
7.1%
G 15788
 
6.8%
J 14987
 
6.5%
A 6654
 
2.9%
E 6071
 
2.6%
C 5838
 
2.5%
B 4561
 
2.0%
Other values (7) 10884
 
4.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 231218
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
S 62966
27.2%
P 58871
25.5%
R 28218
12.2%
M 16380
 
7.1%
G 15788
 
6.8%
J 14987
 
6.5%
A 6654
 
2.9%
E 6071
 
2.6%
C 5838
 
2.5%
B 4561
 
2.0%
Other values (7) 10884
 
4.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 231218
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
S 62966
27.2%
P 58871
25.5%
R 28218
12.2%
M 16380
 
7.1%
G 15788
 
6.8%
J 14987
 
6.5%
A 6654
 
2.9%
E 6071
 
2.6%
C 5838
 
2.5%
B 4561
 
2.0%
Other values (7) 10884
 
4.7%

order_item_id
Real number (ℝ)

Distinct21
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.194535028
Minimum1
Maximum21
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size903.3 KiB
2025-08-06T13:53:27.143265image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q31
95-th percentile2
Maximum21
Range20
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.6859258845
Coefficient of variation (CV)0.5742199841
Kurtosis93.44080835
Mean1.194535028
Median Absolute Deviation (MAD)0
Skewness7.195205955
Sum138099
Variance0.470494319
MonotonicityNot monotonic
2025-08-06T13:53:27.215891image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
1 101340
87.7%
2 10055
 
8.7%
3 2326
 
2.0%
4 969
 
0.8%
5 458
 
0.4%
6 256
 
0.2%
7 60
 
0.1%
8 35
 
< 0.1%
9 28
 
< 0.1%
10 25
 
< 0.1%
Other values (11) 57
 
< 0.1%
ValueCountFrequency (%)
1 101340
87.7%
2 10055
 
8.7%
3 2326
 
2.0%
4 969
 
0.8%
5 458
 
0.4%
ValueCountFrequency (%)
21 1
< 0.1%
20 2
< 0.1%
19 2
< 0.1%
18 2
< 0.1%
17 2
< 0.1%
Distinct32171
Distinct (%)27.8%
Missing0
Missing (%)0.0%
Memory size903.3 KiB
2025-08-06T13:53:27.366054image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length32
Median length32
Mean length32
Min length32

Characters and Unicode

Total characters3699488
Distinct characters16
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique16932 ?
Unique (%)14.6%

Sample

1st row87285b34884572647811a353c7ac498a
2nd row87285b34884572647811a353c7ac498a
3rd row87285b34884572647811a353c7ac498a
4th row87285b34884572647811a353c7ac498a
5th row87285b34884572647811a353c7ac498a
ValueCountFrequency (%)
aca2eb7d00ea1a7b8ebd4e68314663af 533
 
0.5%
99a4788cb24856965c36a24e339b6058 517
 
0.4%
422879e10f46682990de24d770e7f83d 507
 
0.4%
389d119b48cf3043d311335e499d9c6b 405
 
0.4%
368c6c730842d78016ad823897a372db 395
 
0.3%
53759a2ecddad2bb87a079a1f1519f73 389
 
0.3%
d1c427060a0f73f6b889a5c7c61f2ac4 354
 
0.3%
53b36df67ebb7c41585e8d54d6772e08 324
 
0.3%
154e7e31ebfa092203795c972e5804a6 294
 
0.3%
3dd2a17168ec895c781a9191c1e95ad7 276
 
0.2%
Other values (32161) 111615
96.5%
2025-08-06T13:53:27.583404image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
3 237911
 
6.4%
9 235951
 
6.4%
e 233656
 
6.3%
7 233105
 
6.3%
8 232719
 
6.3%
4 231409
 
6.3%
a 231308
 
6.3%
c 231075
 
6.2%
2 230954
 
6.2%
6 230872
 
6.2%
Other values (6) 1370528
37.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3699488
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
3 237911
 
6.4%
9 235951
 
6.4%
e 233656
 
6.3%
7 233105
 
6.3%
8 232719
 
6.3%
4 231409
 
6.3%
a 231308
 
6.3%
c 231075
 
6.2%
2 230954
 
6.2%
6 230872
 
6.2%
Other values (6) 1370528
37.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3699488
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
3 237911
 
6.4%
9 235951
 
6.4%
e 233656
 
6.3%
7 233105
 
6.3%
8 232719
 
6.3%
4 231409
 
6.3%
a 231308
 
6.3%
c 231075
 
6.2%
2 230954
 
6.2%
6 230872
 
6.2%
Other values (6) 1370528
37.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3699488
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
3 237911
 
6.4%
9 235951
 
6.4%
e 233656
 
6.3%
7 233105
 
6.3%
8 232719
 
6.3%
4 231409
 
6.3%
a 231308
 
6.3%
c 231075
 
6.2%
2 230954
 
6.2%
6 230872
 
6.2%
Other values (6) 1370528
37.0%
Distinct3028
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Memory size903.3 KiB
2025-08-06T13:53:27.704296image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length32
Median length32
Mean length32
Min length32

Characters and Unicode

Total characters3699488
Distinct characters16
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique485 ?
Unique (%)0.4%

Sample

1st row3504c0cb71d7fa48d967e0e4c94d59d9
2nd row3504c0cb71d7fa48d967e0e4c94d59d9
3rd row3504c0cb71d7fa48d967e0e4c94d59d9
4th row3504c0cb71d7fa48d967e0e4c94d59d9
5th row3504c0cb71d7fa48d967e0e4c94d59d9
ValueCountFrequency (%)
4a3ca9315b744ce9f8e9374361493884 2128
 
1.8%
6560211a19b47992c3666cc44a7e94c0 2111
 
1.8%
1f50f920176fa81dab994f9023523100 2009
 
1.7%
cc419e0650a3c5ba77189a1882b7556a 1885
 
1.6%
da8622b14eb17ae2831f4ac5b9dab84a 1656
 
1.4%
955fee9216a65b617aa5c0531780ce60 1517
 
1.3%
1025f0e2d44d7041d6cf58b6550e0bfa 1465
 
1.3%
7c67e1448b00f6e969d365cea6b010ab 1454
 
1.3%
7a67c85e85bb2ce8582c35f2203ad736 1236
 
1.1%
ea8482cd71df3c1969d7b9473ff13abc 1233
 
1.1%
Other values (3018) 98915
85.6%
2025-08-06T13:53:28.084180image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 251950
 
6.8%
c 243876
 
6.6%
4 243091
 
6.6%
6 238193
 
6.4%
0 237394
 
6.4%
a 236721
 
6.4%
b 235816
 
6.4%
3 235558
 
6.4%
9 228810
 
6.2%
2 227464
 
6.1%
Other values (6) 1320615
35.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3699488
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 251950
 
6.8%
c 243876
 
6.6%
4 243091
 
6.6%
6 238193
 
6.4%
0 237394
 
6.4%
a 236721
 
6.4%
b 235816
 
6.4%
3 235558
 
6.4%
9 228810
 
6.2%
2 227464
 
6.1%
Other values (6) 1320615
35.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3699488
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 251950
 
6.8%
c 243876
 
6.6%
4 243091
 
6.6%
6 238193
 
6.4%
0 237394
 
6.4%
a 236721
 
6.4%
b 235816
 
6.4%
3 235558
 
6.4%
9 228810
 
6.2%
2 227464
 
6.1%
Other values (6) 1320615
35.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3699488
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 251950
 
6.8%
c 243876
 
6.6%
4 243091
 
6.6%
6 238193
 
6.4%
0 237394
 
6.4%
a 236721
 
6.4%
b 235816
 
6.4%
3 235558
 
6.4%
9 228810
 
6.2%
2 227464
 
6.1%
Other values (6) 1320615
35.7%
Distinct91386
Distinct (%)79.0%
Missing0
Missing (%)0.0%
Memory size903.3 KiB
2025-08-06T13:53:28.255880image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length19
Median length19
Mean length19
Min length19

Characters and Unicode

Total characters2196571
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique75453 ?
Unique (%)65.3%

Sample

1st row2017-10-06 11:07:15
2nd row2017-10-06 11:07:15
3rd row2017-10-06 11:07:15
4th row2017-08-21 20:05:16
5th row2017-08-08 18:37:31
ValueCountFrequency (%)
2017-11-30 1712
 
0.7%
2017-12-07 782
 
0.3%
2018-04-19 709
 
0.3%
2018-08-07 672
 
0.3%
2018-05-10 670
 
0.3%
2018-01-18 667
 
0.3%
2018-03-08 665
 
0.3%
2018-03-22 658
 
0.3%
2018-03-01 654
 
0.3%
2018-02-22 650
 
0.3%
Other values (40183) 223379
96.6%
2025-08-06T13:53:28.478840image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 370760
16.9%
1 355888
16.2%
2 283047
12.9%
- 231218
10.5%
: 231218
10.5%
8 116247
 
5.3%
115609
 
5.3%
3 111525
 
5.1%
5 109560
 
5.0%
7 99644
 
4.5%
Other values (3) 171855
7.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2196571
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 370760
16.9%
1 355888
16.2%
2 283047
12.9%
- 231218
10.5%
: 231218
10.5%
8 116247
 
5.3%
115609
 
5.3%
3 111525
 
5.1%
5 109560
 
5.0%
7 99644
 
4.5%
Other values (3) 171855
7.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2196571
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 370760
16.9%
1 355888
16.2%
2 283047
12.9%
- 231218
10.5%
: 231218
10.5%
8 116247
 
5.3%
115609
 
5.3%
3 111525
 
5.1%
5 109560
 
5.0%
7 99644
 
4.5%
Other values (3) 171855
7.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2196571
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 370760
16.9%
1 355888
16.2%
2 283047
12.9%
- 231218
10.5%
: 231218
10.5%
8 116247
 
5.3%
115609
 
5.3%
3 111525
 
5.1%
5 109560
 
5.0%
7 99644
 
4.5%
Other values (3) 171855
7.8%

price
Real number (ℝ)

Distinct5879
Distinct (%)5.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean120.6198496
Minimum0.85
Maximum6735
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size903.3 KiB
2025-08-06T13:53:28.578200image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0.85
5-th percentile17
Q139.9
median74.9
Q3134.9
95-th percentile349.9
Maximum6735
Range6734.15
Interquartile range (IQR)95

Descriptive statistics

Standard deviation182.653476
Coefficient of variation (CV)1.514290365
Kurtosis107.9051006
Mean120.6198496
Median Absolute Deviation (MAD)42
Skewness7.615418217
Sum13944740.19
Variance33362.2923
MonotonicityNot monotonic
2025-08-06T13:53:28.689931image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
59.9 2574
 
2.2%
69.9 2096
 
1.8%
49.9 2013
 
1.7%
89.9 1604
 
1.4%
99.9 1505
 
1.3%
29.9 1362
 
1.2%
39.9 1325
 
1.1%
79.9 1266
 
1.1%
19.9 1263
 
1.1%
29.99 1204
 
1.0%
Other values (5869) 99397
86.0%
ValueCountFrequency (%)
0.85 3
 
< 0.1%
1.2 20
< 0.1%
2.2 2
 
< 0.1%
2.29 1
 
< 0.1%
2.9 1
 
< 0.1%
ValueCountFrequency (%)
6735 1
< 0.1%
6499 1
< 0.1%
4799 1
< 0.1%
4690 1
< 0.1%
4590 1
< 0.1%

freight_value
Real number (ℝ)

Distinct6954
Distinct (%)6.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20.05687957
Minimum0
Maximum409.68
Zeros387
Zeros (%)0.3%
Negative0
Negative (%)0.0%
Memory size903.3 KiB
2025-08-06T13:53:28.804925image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile7.78
Q113.08
median16.32
Q321.21
95-th percentile45.31
Maximum409.68
Range409.68
Interquartile range (IQR)8.13

Descriptive statistics

Standard deviation15.83618425
Coefficient of variation (CV)0.7895637103
Kurtosis58.25004831
Mean20.05687957
Median Absolute Deviation (MAD)3.63
Skewness5.56021276
Sum2318755.79
Variance250.7847315
MonotonicityNot monotonic
2025-08-06T13:53:28.901601image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
15.1 3754
 
3.2%
7.78 2281
 
2.0%
11.85 1948
 
1.7%
14.1 1939
 
1.7%
18.23 1599
 
1.4%
7.39 1554
 
1.3%
16.11 1185
 
1.0%
15.23 1041
 
0.9%
8.72 950
 
0.8%
16.79 902
 
0.8%
Other values (6944) 98456
85.2%
ValueCountFrequency (%)
0 387
0.3%
0.01 4
 
< 0.1%
0.02 3
 
< 0.1%
0.03 14
 
< 0.1%
0.04 4
 
< 0.1%
ValueCountFrequency (%)
409.68 1
< 0.1%
375.28 2
< 0.1%
339.59 1
< 0.1%
338.3 1
< 0.1%
322.1 1
< 0.1%
Distinct71
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size903.3 KiB
2025-08-06T13:53:29.034190image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length46
Median length30
Mean length14.87506163
Min length3

Characters and Unicode

Total characters1719691
Distinct characters28
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowutilidades_domesticas
2nd rowutilidades_domesticas
3rd rowutilidades_domesticas
4th rowutilidades_domesticas
5th rowutilidades_domesticas
ValueCountFrequency (%)
cama_mesa_banho 11847
 
10.2%
beleza_saude 9944
 
8.6%
esporte_lazer 8942
 
7.7%
moveis_decoracao 8743
 
7.6%
informatica_acessorios 8105
 
7.0%
utilidades_domesticas 7331
 
6.3%
relogios_presentes 6161
 
5.3%
telefonia 4692
 
4.1%
ferramentas_jardim 4558
 
3.9%
automotivo 4356
 
3.8%
Other values (61) 40930
35.4%
2025-08-06T13:53:29.288512image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 208749
12.1%
a 206939
12.0%
s 171469
10.0%
o 170145
9.9%
i 114273
 
6.6%
r 110581
 
6.4%
_ 109454
 
6.4%
t 82596
 
4.8%
c 81711
 
4.8%
m 77941
 
4.5%
Other values (18) 385833
22.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1719691
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 208749
12.1%
a 206939
12.0%
s 171469
10.0%
o 170145
9.9%
i 114273
 
6.6%
r 110581
 
6.4%
_ 109454
 
6.4%
t 82596
 
4.8%
c 81711
 
4.8%
m 77941
 
4.5%
Other values (18) 385833
22.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1719691
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 208749
12.1%
a 206939
12.0%
s 171469
10.0%
o 170145
9.9%
i 114273
 
6.6%
r 110581
 
6.4%
_ 109454
 
6.4%
t 82596
 
4.8%
c 81711
 
4.8%
m 77941
 
4.5%
Other values (18) 385833
22.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1719691
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 208749
12.1%
a 206939
12.0%
s 171469
10.0%
o 170145
9.9%
i 114273
 
6.6%
r 110581
 
6.4%
_ 109454
 
6.4%
t 82596
 
4.8%
c 81711
 
4.8%
m 77941
 
4.5%
Other values (18) 385833
22.4%

product_name_lenght
Real number (ℝ)

Distinct66
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean48.76654067
Minimum5
Maximum76
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size903.3 KiB
2025-08-06T13:53:29.389579image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile29
Q142
median52
Q357
95-th percentile60
Maximum76
Range71
Interquartile range (IQR)15

Descriptive statistics

Standard deviation10.034187
Coefficient of variation (CV)0.2057596636
Kurtosis0.1495529108
Mean48.76654067
Median Absolute Deviation (MAD)6
Skewness-0.9054196855
Sum5637851
Variance100.6849088
MonotonicityNot monotonic
2025-08-06T13:53:29.513218image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
59 8598
 
7.4%
60 8004
 
6.9%
56 6803
 
5.9%
58 6753
 
5.8%
57 6261
 
5.4%
55 5797
 
5.0%
54 5469
 
4.7%
53 4319
 
3.7%
52 4280
 
3.7%
49 3670
 
3.2%
Other values (56) 55655
48.1%
ValueCountFrequency (%)
5 9
< 0.1%
6 3
 
< 0.1%
7 2
 
< 0.1%
8 4
 
< 0.1%
9 14
< 0.1%
ValueCountFrequency (%)
76 1
 
< 0.1%
72 9
< 0.1%
69 1
 
< 0.1%
68 1
 
< 0.1%
67 3
 
< 0.1%
Distinct2958
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean785.8081983
Minimum4
Maximum3992
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size903.3 KiB
2025-08-06T13:53:29.628688image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile160
Q1346
median600
Q3983
95-th percentile2120
Maximum3992
Range3988
Interquartile range (IQR)637

Descriptive statistics

Standard deviation652.4186191
Coefficient of variation (CV)0.8302517338
Kurtosis4.927244637
Mean785.8081983
Median Absolute Deviation (MAD)295
Skewness2.011533038
Sum90846500
Variance425650.0546
MonotonicityNot monotonic
2025-08-06T13:53:29.748771image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
341 708
 
0.6%
1893 664
 
0.6%
348 643
 
0.6%
492 590
 
0.5%
903 588
 
0.5%
245 576
 
0.5%
366 532
 
0.5%
236 513
 
0.4%
340 484
 
0.4%
919 436
 
0.4%
Other values (2948) 109875
95.0%
ValueCountFrequency (%)
4 6
< 0.1%
8 2
 
< 0.1%
15 1
 
< 0.1%
20 7
< 0.1%
23 1
 
< 0.1%
ValueCountFrequency (%)
3992 2
 
< 0.1%
3988 1
 
< 0.1%
3985 3
< 0.1%
3976 6
< 0.1%
3963 1
 
< 0.1%

product_photos_qty
Real number (ℝ)

Distinct19
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.205373284
Minimum1
Maximum20
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size903.3 KiB
2025-08-06T13:53:29.853200image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q33
95-th percentile6
Maximum20
Range19
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.717770987
Coefficient of variation (CV)0.778902601
Kurtosis4.837255564
Mean2.205373284
Median Absolute Deviation (MAD)0
Skewness1.910867972
Sum254961
Variance2.950737165
MonotonicityNot monotonic
2025-08-06T13:53:29.955551image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
1 58429
50.5%
2 22894
 
19.8%
3 12869
 
11.1%
4 8770
 
7.6%
5 5558
 
4.8%
6 3905
 
3.4%
7 1552
 
1.3%
8 771
 
0.7%
10 353
 
0.3%
9 309
 
0.3%
Other values (9) 199
 
0.2%
ValueCountFrequency (%)
1 58429
50.5%
2 22894
 
19.8%
3 12869
 
11.1%
4 8770
 
7.6%
5 5558
 
4.8%
ValueCountFrequency (%)
20 1
 
< 0.1%
19 2
 
< 0.1%
18 4
 
< 0.1%
17 11
< 0.1%
15 12
< 0.1%

product_weight_g
Real number (ℝ)

Distinct2197
Distinct (%)1.9%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean2113.907697
Minimum0
Maximum40425
Zeros8
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size903.3 KiB
2025-08-06T13:53:30.102684image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile125
Q1300
median700
Q31800
95-th percentile9850
Maximum40425
Range40425
Interquartile range (IQR)1500

Descriptive statistics

Standard deviation3781.754895
Coefficient of variation (CV)1.788987713
Kurtosis16.03454362
Mean2113.907697
Median Absolute Deviation (MAD)500
Skewness3.580649802
Sum244384641
Variance14301670.09
MonotonicityNot monotonic
2025-08-06T13:53:30.241543image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
200 6815
 
5.9%
150 5329
 
4.6%
250 4671
 
4.0%
300 4313
 
3.7%
100 3571
 
3.1%
400 3481
 
3.0%
350 3211
 
2.8%
500 2787
 
2.4%
600 2778
 
2.4%
700 2095
 
1.8%
Other values (2187) 76557
66.2%
ValueCountFrequency (%)
0 8
 
< 0.1%
2 5
 
< 0.1%
25 3
 
< 0.1%
50 985
0.9%
53 2
 
< 0.1%
ValueCountFrequency (%)
40425 3
 
< 0.1%
30000 296
0.3%
29800 1
 
< 0.1%
29750 1
 
< 0.1%
29700 4
 
< 0.1%

product_length_cm
Real number (ℝ)

Distinct99
Distinct (%)0.1%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean30.30790257
Minimum7
Maximum105
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size903.3 KiB
2025-08-06T13:53:30.367894image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum7
5-th percentile16
Q118
median25
Q338
95-th percentile62
Maximum105
Range98
Interquartile range (IQR)20

Descriptive statistics

Standard deviation16.21110845
Coefficient of variation (CV)0.5348805782
Kurtosis3.663122897
Mean30.30790257
Median Absolute Deviation (MAD)8
Skewness1.742509872
Sum3503836
Variance262.8000372
MonotonicityNot monotonic
2025-08-06T13:53:30.494112image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
16 17712
 
15.3%
20 10603
 
9.2%
30 7814
 
6.8%
17 6126
 
5.3%
18 5844
 
5.1%
19 4845
 
4.2%
25 4768
 
4.1%
40 4238
 
3.7%
22 3935
 
3.4%
50 3091
 
2.7%
Other values (89) 46632
40.3%
ValueCountFrequency (%)
7 32
 
< 0.1%
8 2
 
< 0.1%
9 4
 
< 0.1%
10 8
 
< 0.1%
11 96
0.1%
ValueCountFrequency (%)
105 331
0.3%
104 30
 
< 0.1%
103 45
 
< 0.1%
102 60
 
0.1%
101 108
 
0.1%

product_height_cm
Real number (ℝ)

Distinct102
Distinct (%)0.1%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean16.63847658
Minimum2
Maximum105
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size903.3 KiB
2025-08-06T13:53:30.963986image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile3
Q18
median13
Q320
95-th percentile45
Maximum105
Range103
Interquartile range (IQR)12

Descriptive statistics

Standard deviation13.47356966
Coefficient of variation (CV)0.8097838523
Kurtosis7.287507772
Mean16.63847658
Median Absolute Deviation (MAD)6
Skewness2.243121061
Sum1923541
Variance181.5370793
MonotonicityNot monotonic
2025-08-06T13:53:31.068591image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10 10208
 
8.8%
20 6796
 
5.9%
15 6784
 
5.9%
11 6309
 
5.5%
12 6196
 
5.4%
2 5097
 
4.4%
4 4778
 
4.1%
8 4773
 
4.1%
16 4661
 
4.0%
5 4636
 
4.0%
Other values (92) 55370
47.9%
ValueCountFrequency (%)
2 5097
4.4%
3 2770
2.4%
4 4778
4.1%
5 4636
4.0%
6 3511
3.0%
ValueCountFrequency (%)
105 137
0.1%
104 14
 
< 0.1%
103 49
 
< 0.1%
102 10
 
< 0.1%
100 42
 
< 0.1%

product_width_cm
Real number (ℝ)

Distinct95
Distinct (%)0.1%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean23.11316691
Minimum6
Maximum118
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size903.3 KiB
2025-08-06T13:53:31.168546image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum6
5-th percentile11
Q115
median20
Q330
95-th percentile45
Maximum118
Range112
Interquartile range (IQR)15

Descriptive statistics

Standard deviation11.75508326
Coefficient of variation (CV)0.5085881698
Kurtosis4.553189649
Mean23.11316691
Median Absolute Deviation (MAD)6
Skewness1.707217058
Sum2672067
Variance138.1819824
MonotonicityNot monotonic
2025-08-06T13:53:31.268980image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20 12447
 
10.8%
11 10662
 
9.2%
15 8964
 
7.8%
16 8677
 
7.5%
30 7880
 
6.8%
12 5590
 
4.8%
13 5412
 
4.7%
14 4753
 
4.1%
18 4122
 
3.6%
40 4042
 
3.5%
Other values (85) 43059
37.2%
ValueCountFrequency (%)
6 2
 
< 0.1%
7 5
 
< 0.1%
8 29
 
< 0.1%
9 51
< 0.1%
10 83
0.1%
ValueCountFrequency (%)
118 7
< 0.1%
105 14
< 0.1%
104 1
 
< 0.1%
103 1
 
< 0.1%
102 2
 
< 0.1%
Distinct71
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size903.3 KiB
2025-08-06T13:53:31.401525image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length39
Median length31
Mean length12.98994888
Min length3

Characters and Unicode

Total characters1501755
Distinct characters25
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowhousewares
2nd rowhousewares
3rd rowhousewares
4th rowhousewares
5th rowhousewares
ValueCountFrequency (%)
bed_bath_table 11847
 
10.2%
health_beauty 9944
 
8.6%
sports_leisure 8942
 
7.7%
furniture_decor 8743
 
7.6%
computers_accessories 8105
 
7.0%
housewares 7331
 
6.3%
watches_gifts 6161
 
5.3%
telephony 4692
 
4.1%
garden_tools 4558
 
3.9%
auto 4356
 
3.8%
Other values (61) 40930
35.4%
2025-08-06T13:53:31.617077image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 183852
12.2%
s 141050
 
9.4%
t 132154
 
8.8%
o 111010
 
7.4%
r 105008
 
7.0%
a 101597
 
6.8%
_ 101311
 
6.7%
u 77584
 
5.2%
c 71956
 
4.8%
i 62806
 
4.2%
Other values (15) 413427
27.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1501755
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 183852
12.2%
s 141050
 
9.4%
t 132154
 
8.8%
o 111010
 
7.4%
r 105008
 
7.0%
a 101597
 
6.8%
_ 101311
 
6.7%
u 77584
 
5.2%
c 71956
 
4.8%
i 62806
 
4.2%
Other values (15) 413427
27.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1501755
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 183852
12.2%
s 141050
 
9.4%
t 132154
 
8.8%
o 111010
 
7.4%
r 105008
 
7.0%
a 101597
 
6.8%
_ 101311
 
6.7%
u 77584
 
5.2%
c 71956
 
4.8%
i 62806
 
4.2%
Other values (15) 413427
27.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1501755
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 183852
12.2%
s 141050
 
9.4%
t 132154
 
8.8%
o 111010
 
7.4%
r 105008
 
7.0%
a 101597
 
6.8%
_ 101311
 
6.7%
u 77584
 
5.2%
c 71956
 
4.8%
i 62806
 
4.2%
Other values (15) 413427
27.5%

review_score
Real number (ℝ)

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.034409086
Minimum1
Maximum5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size903.3 KiB
2025-08-06T13:53:31.679613image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q14
median5
Q35
95-th percentile5
Maximum5
Range4
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.385583715
Coefficient of variation (CV)0.3434415513
Kurtosis0.2153281851
Mean4.034409086
Median Absolute Deviation (MAD)0
Skewness-1.275224718
Sum466414
Variance1.919842231
MonotonicityNot monotonic
2025-08-06T13:53:31.746341image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=5)
ValueCountFrequency (%)
5 65374
56.5%
4 21951
 
19.0%
1 14546
 
12.6%
3 9718
 
8.4%
2 4020
 
3.5%
ValueCountFrequency (%)
1 14546
 
12.6%
2 4020
 
3.5%
3 9718
 
8.4%
4 21951
 
19.0%
5 65374
56.5%
ValueCountFrequency (%)
5 65374
56.5%
4 21951
 
19.0%
3 9718
 
8.4%
2 4020
 
3.5%
1 14546
 
12.6%
Distinct35176
Distinct (%)71.9%
Missing66703
Missing (%)57.7%
Memory size903.3 KiB
2025-08-06T13:53:31.959157image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length208
Median length158
Mean length70.20514865
Min length1

Characters and Unicode

Total characters3433453
Distinct characters205
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique28730 ?
Unique (%)58.7%

Sample

1st rowNão testei o produto ainda, mas ele veio correto e em boas condições. Apenas a caixa que veio bem amassada e danificada, o que ficará chato, pois se trata de um presente.
2nd rowNão testei o produto ainda, mas ele veio correto e em boas condições. Apenas a caixa que veio bem amassada e danificada, o que ficará chato, pois se trata de um presente.
3rd rowNão testei o produto ainda, mas ele veio correto e em boas condições. Apenas a caixa que veio bem amassada e danificada, o que ficará chato, pois se trata de um presente.
4th rowDeveriam embalar melhor o produto. A caixa veio toda amassada e vou dar de presente.
5th rowSó achei ela pequena pra seis xícaras ,mais é um bom produto
ValueCountFrequency (%)
o 22075
 
3.8%
produto 20487
 
3.5%
e 19397
 
3.3%
a 14735
 
2.5%
de 14075
 
2.4%
do 12785
 
2.2%
não 12702
 
2.2%
que 10115
 
1.7%
prazo 9242
 
1.6%
muito 8930
 
1.5%
Other values (19329) 441610
75.3%
2025-08-06T13:53:32.285325image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
543604
15.8%
o 338902
 
9.9%
e 329088
 
9.6%
a 272669
 
7.9%
r 193607
 
5.6%
i 158463
 
4.6%
t 156573
 
4.6%
d 145458
 
4.2%
n 133224
 
3.9%
s 129633
 
3.8%
Other values (195) 1032232
30.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3433453
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
543604
15.8%
o 338902
 
9.9%
e 329088
 
9.6%
a 272669
 
7.9%
r 193607
 
5.6%
i 158463
 
4.6%
t 156573
 
4.6%
d 145458
 
4.2%
n 133224
 
3.9%
s 129633
 
3.8%
Other values (195) 1032232
30.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3433453
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
543604
15.8%
o 338902
 
9.9%
e 329088
 
9.6%
a 272669
 
7.9%
r 193607
 
5.6%
i 158463
 
4.6%
t 156573
 
4.6%
d 145458
 
4.2%
n 133224
 
3.9%
s 129633
 
3.8%
Other values (195) 1032232
30.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3433453
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
543604
15.8%
o 338902
 
9.9%
e 329088
 
9.6%
a 272669
 
7.9%
r 193607
 
5.6%
i 158463
 
4.6%
t 156573
 
4.6%
d 145458
 
4.2%
n 133224
 
3.9%
s 129633
 
3.8%
Other values (195) 1032232
30.1%